Unlike most plants, members of the genus Solanum produce cholesterol and use this as a precursor for steroidal glycoalkaloids. The production of the compounds begins as a branch from brassinosteroid biosynthesis, whic...Unlike most plants, members of the genus Solanum produce cholesterol and use this as a precursor for steroidal glycoalkaloids. The production of the compounds begins as a branch from brassinosteroid biosynthesis, which produces cholesterol that is further modified to produce steroidal glycoalkaloids. During the cholesterol biosynthesis pathway, genetic engineering could alter the formation of cholesterol from provitamin D3(7-dehydrocholesterol) and produce vitamin D3. Cholesterol is a precursor for many steroidal glycoalkaloids, including a-tomatine and esculeoside A. Alpha-tomatine is consumed by mammals and it can reduce cholesterol content and improve LDL:HDL ratio. When there is a high a-tomatine content, the fruit will have a bitter flavor, which together with other steroidal glycoalkaloids serving as protective and defensive compounds for tomato against insect, fungal, and bacterial pests. These compounds also affect the rhizosphere bacteria by recruiting beneficial bacteria. One of the steroidal glycoalkaloids, esculeoside A increases while fruit ripening. This review focuses on recent studies that uncovered key reactions of the production of cholesterol and steroidal glycoalkaloids in tomato connecting to human health, fruit flavor, and plant defense and the potential application for tomato crop improvement.展开更多
Highlights ZmMYC2 promoter contains favorable haplotypes selected during domestication,enhancing its expression level in modern maize.ZmMYC2 may balance the trade-off between growth and defense via jasmonate and auxin...Highlights ZmMYC2 promoter contains favorable haplotypes selected during domestication,enhancing its expression level in modern maize.ZmMYC2 may balance the trade-off between growth and defense via jasmonate and auxin signaling pathways.ZmMYC2 regulates drought-response genes(CER2 and TIP3c)to optimize drought stress resilience.展开更多
The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address th...The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address this critical challenge,this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient(ZSG-MAD3PG).The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient(MAD3PG)algorithm and incorporates defensive deception(DD)strategies to achieve adaptive and efficient protection.While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,the proposed ZSG-MAD3PG framework mitigates these limitations through multi-stage game modeling and adaptive learning,enabling more efficient resource utilization and faster response times.The Stackelberg-based architecture allows defenders to dynamically optimize packet sampling strategies,while attackers adjust their tactics to reach rapid equilibrium.Furthermore,dynamic deception techniques reduce the time required for the concealment of attacks and the overall system burden.A lightweight behavioral fingerprinting detection mechanism further enhances real-time zero-day attack identification within industrial device clusters.ZSG-MAD3PG demonstrates higher true positive rates(TPR)and lower false alarm rates(FAR)compared to existing methods,while also achieving improved latency,resource efficiency,and stealth adaptability in IIoT zero-day defense scenarios.展开更多
Heat stress hinders the growth and productivity of sweetpotato plants,predominantly through oxidative damage to cellular membranes.Therefore,the development of efficient approaches for mitigating heat-related impairme...Heat stress hinders the growth and productivity of sweetpotato plants,predominantly through oxidative damage to cellular membranes.Therefore,the development of efficient approaches for mitigating heat-related impairments is essential for the long-term production of sweetpotatoes.Melatonin has been recognised for its capacity to assist plants in dealing with abiotic stress conditions.This research aimed to investigate how different doses of exogenous melatonin influence heat damage in sweetpotato plants.Heat stress drastically affected shoot and root fresh weight by 31.8 and 44.5%,respectively.This reduction resulted in oxidative stress characterised by increased formation of hydrogen peroxide(H_(2)O_(2))by 804.4%,superoxide ion(O_(2)^(·-))by 211.5%and malondialdehyde(MDA)by 234.2%.Heat stress also reduced chlorophyll concentration,photosystemⅡefficiency(F_v/F_m)by 15.3%and gaseous exchange.However,pre-treatment with 100μmol L^(-1)melatonin increased growth and reduced oxidative damage to sweetpotato plants under heat stress.In particular,melatonin decreased H_(2)O_(2),O_(2)^(·-)and MDA by 64.8%,42.7%and 38.2%,respectively.Melatonin also mitigated the decline in chlorophyll levels and improved stomatal traits,gaseous exchange and F_(v)/F_(m)(13%).Results suggested that the favorable outcomes of melatonin treatment can be associated with elevated antioxidant enzyme activity and an increase in non-enzymatic antioxidants and osmo-protectants.Overall,these findings indicate that exogenous melatonin can improve heat stress tolerance in sweetpotatoes.This stu dy will assist re searchers in further investigating how melatonin makes sweetpotatoes more resistant to heat stress.展开更多
Due to the discharge of industrialwastewater,urban domestic sewage,and intensive marine aquaculture tailwater,nitrate(NO_(3)^(−))pollution has emerged as a significant issue in offshore waters.Nitrate pollution affect...Due to the discharge of industrialwastewater,urban domestic sewage,and intensive marine aquaculture tailwater,nitrate(NO_(3)^(−))pollution has emerged as a significant issue in offshore waters.Nitrate pollution affects aquatic life and may interact with other pollutants,leading to comprehensive toxicity.Cadmium(Cd^(2+))is the most widespread metal contaminant,adversely affecting aquatic life in the coastal waters of China.Despite this,few studies have focused on the synergistic toxicity of NO_(3)^(−)and Cd^(2+)in marine organisms.This study conducted a 30-day exposure experiment on marine Japanese flounder(Paralichthys olivaceus)to explore the synergistic toxicity of NO_(3)^(−)and Cd^(2+).Our results demonstrated that the exposure to Cd^(2+)alone induced slight histopathological changes in the liver.However,malformations such as hepatic vacuolar degeneration and sinusoid dilatationwere exacerbated under co-exposure.Moreover,co-exposure induced the downregulation of antioxidants and the upregulation of the product malonaldehyde(MDA)from lipid peroxidation,indicating potent oxidative stress in the liver.The increased mRNA expression of IL-8,TNF-α,and IL-1β,along with the decreased expression level of TGF-β,indicated a synergistic inflammatory response in the organisms.Furthermore,the co-exposure led to an abnormal expression of P53,caspase-3,caspase-9,Bcl-2,and Bax,and disturbed the apoptosis in the liver through TUNEL staining analysis.Overall,our results imply that co-exposure synergistically affects inflammation,redox status,and apoptosis in flounders.Therefore,the findings from this study provide valuable perspectives on the ecological risk assessment of marine teleosts co-exposure to NO_(3)^(−)and Cd^(2+).展开更多
In recent years,China and Indonesia have made notable progress in multiple areas of security cooperation,and their collaboration in this respect has continued to deepen under the leadership of both countries.In Novemb...In recent years,China and Indonesia have made notable progress in multiple areas of security cooperation,and their collaboration in this respect has continued to deepen under the leadership of both countries.In November 2024,China and Indonesia issued a joint statement during Indonesian President Prabowo Subianto’s visit to China.It was his first overseas trip after his inauguration.In the statement,the two countries agreed to add security cooperation as the fifth pillar of their partnership,marking an upgrade of bilateral ties.展开更多
In a Nature Physics report published in late September 2024[1],a team of scientists and engineers at Sandia National Laboratories(Albuquerque,NM,USA)described the results of a laboratory experiment showing that a nucl...In a Nature Physics report published in late September 2024[1],a team of scientists and engineers at Sandia National Laboratories(Albuquerque,NM,USA)described the results of a laboratory experiment showing that a nuclear blast could create a burst of X-rays powerful enough to change the path of a large asteroid that might one day be on a collision course with Earth.展开更多
Benzoxazinoids(BXDs)are a class of plant secondary metabolites that play pivotal roles in plant defense against pathogens and pests,as well as in allelopathy.This review synthesizes recent advances in our understandin...Benzoxazinoids(BXDs)are a class of plant secondary metabolites that play pivotal roles in plant defense against pathogens and pests,as well as in allelopathy.This review synthesizes recent advances in our understanding of the structural and functional diversity of BXDs,the independent evolutionary trajectories of their biosynthetic pathways across different plant species,their metabolic transformations in target organisms,and the opportunities and challenges of optimizing BXD biosynthesis in crops through metabolic engineering.Compared with monocotyledons,dicotyledons employ a more diverse set of enzymes to catalyze the core reactions of BXD biosynthesis.This functional divergence—yet biochemical convergence—between monocotyledons and dicotyledons exemplifies the convergent evolution of BXD biosynthetic pathways in plants.BXDs act not only as potent antifeedants,insecticides,and antimicrobials but also function as signaling molecules that induce callose deposition and activate systemic immunity,thereby enhancing plant resistance to biotic stress.Furthermore,BXDs shape the rhizosphere by modulating microbial communities through species-specific antimicrobial activities and microbial detoxification mechanisms,ultimately exerting allelopathic effects that alter soil chemistry and nutrient dynamics.The translational potential of BXDs is increasingly recognized by synthetic biology approaches,including artificial intelligence-driven enzyme optimization,heterologous pathway engineering,and gene-editing to enhance crop resistance.Despite these promising prospects,challenges remain in balancing metabolic trade-offs and mitigating ecological risks associated with persistent accumulation of BXDs.Future research integrating multi-omics,evolutionary genomics,and microbiome studies will be essential to fully harness BXDs for sustainable crop improvement and reduced reliance on synthetic agrochemicals.展开更多
Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),t...Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),to enhance structural reasoning,knowledge retrieval,and memory management.The expansion of their application scope imposes higher requirements on the robustness of GNNs.However,as GNNs are applied to more dynamic and heterogeneous environments,they become increasingly vulnerable to real-world perturbations.In particular,graph data frequently encounters joint adversarial perturbations that simultaneously affect both structures and features,which are significantly more challenging than isolated attacks.These disruptions,caused by incomplete data,malicious attacks,or inherent noise,pose substantial threats to the stable and reliable performance of traditional GNN models.To address this issue,this study proposes the Dual-Shield Graph Neural Network(DSGNN),a defense model that simultaneously mitigates structural and feature perturbations.DSGNN utilizes two parallel GNN channels to independently process structural noise and feature noise,and introduces an adaptive fusion mechanism that integrates information from both pathways to generate robust node representations.Theoretical analysis demonstrates that DSGNN achieves a tighter robustness boundary under joint perturbations compared to conventional single-channel methods.Experimental evaluations across Cora,CiteSeer,and Industry datasets show that DSGNN achieves the highest average classification accuracy under various adversarial settings,reaching 81.24%,71.94%,and 81.66%,respectively,outperforming GNNGuard,GCN-Jaccard,GCN-SVD,RGCN,and NoisyGNN.These results underscore the importance of multi-view perturbation decoupling in constructing resilient GNN models for real-world applications.展开更多
GAME15, a scaffold protein, orchestrates the biosynthesis of steroidal glycoalkaloids (a class of compounds with known defensive properties) and steroidal saponins (which contribute to plant defense) in Solanaceae pla...GAME15, a scaffold protein, orchestrates the biosynthesis of steroidal glycoalkaloids (a class of compounds with known defensive properties) and steroidal saponins (which contribute to plant defense) in Solanaceae plants, essential for their defense mechanisms. By assembling key enzymes at the endoplasmic reticulum, GAME15 ensures efficient metabolite production, preventing toxic intermediate diffusion. This breakthrough in plant defense biosynthesis opens opportunities for metabolic engineering, enabling the production of valuable metabolites in non-native hosts and offering potential strategies for crop protection, reducing the need for chemical pesticides.展开更多
Moving Target Defense(MTD)necessitates scientifically effective decision-making methodologies for defensive technology implementation.While most MTD decision studies focus on accurately identifying optimal strategies,...Moving Target Defense(MTD)necessitates scientifically effective decision-making methodologies for defensive technology implementation.While most MTD decision studies focus on accurately identifying optimal strategies,the issue of optimal defense timing remains underexplored.Current default approaches—periodic or overly frequent MTD triggers—lead to suboptimal trade-offs among system security,performance,and cost.The timing of MTD strategy activation critically impacts both defensive efficacy and operational overhead,yet existing frameworks inadequately address this temporal dimension.To bridge this gap,this paper proposes a Stackelberg-FlipIt game model that formalizes asymmetric cyber conflicts as alternating control over attack surfaces,thereby capturing the dynamic security state evolution of MTD systems.We introduce a belief factor to quantify information asymmetry during adversarial interactions,enhancing the precision of MTD trigger timing.Leveraging this game-theoretic foundation,we employMulti-Agent Reinforcement Learning(MARL)to derive adaptive temporal strategies,optimized via a novel four-dimensional reward function that holistically balances security,performance,cost,and timing.Experimental validation using IP addressmutation against scanning attacks demonstrates stable strategy convergence and accelerated defense response,significantly improving cybersecurity affordability and effectiveness.展开更多
In air combat,one effective way to counter an incoming missile attacking an aircraft is to launch a defense missile compared with traditional passive defense strategies such as decoy and electronic countermeasures.To ...In air combat,one effective way to counter an incoming missile attacking an aircraft is to launch a defense missile compared with traditional passive defense strategies such as decoy and electronic countermeasures.To address this issue,this paper proposes a three-body cooperative active defense guidance law with overload constraints from the perspective of a small speed ratio.First,a cooperative guidance-oriented model for active defense is established and linearized to provide a foundation for the design of the guidance law.Then,the essential quantity known as Zero-Effort-Miss(ZEM)is analyzed during the engagement process.In order to minimize the influence of inaccurate estimates of remaining flight time in the ZEM,the concept of Zero-Effort-Velocity(ZEV)is introduced.Subsequently,utilizing the sliding mode control method,the guidance law is designed by selecting the ZEM and ZEV as sliding mode surfaces,combined with the fast power reaching law,and its finite-time stability is analyzed using the Lyapunov method.Furthermore,to quantitatively evaluate the performance of the proposed active defense guidance law,the interception rendezvous angle index is introduced.The proposed active defense guidance law considers integrating information from the incoming missile,aircraft,and defense missile with fewer simplifications and assumptions,and ensures that the aircraft is effectively protected with less overload required for the defense missile.Finally,simulation experiments demonstrate the effectiveness and adaptability of the proposed active defense guidance law.展开更多
The emergence of adversarial examples has revealed the inadequacies in the robustness of image classification models based on Convolutional Neural Networks (CNNs). Particularly in recent years, the discovery of natura...The emergence of adversarial examples has revealed the inadequacies in the robustness of image classification models based on Convolutional Neural Networks (CNNs). Particularly in recent years, the discovery of natural adversarial examples has posed significant challenges, as traditional defense methods against adversarial attacks have proven to be largely ineffective against these natural adversarial examples. This paper explores defenses against these natural adversarial examples from three perspectives: adversarial examples, model architecture, and dataset. First, it employs Class Activation Mapping (CAM) to visualize how models classify natural adversarial examples, identifying several typical attack patterns. Next, various common CNN models are analyzed to evaluate their susceptibility to these attacks, revealing that different architectures exhibit varying defensive capabilities. The study finds that as the depth of a network increases, its defenses against natural adversarial examples strengthen. Lastly, Finally, the impact of dataset class distribution on the defense capability of models is examined, focusing on two aspects: the number of classes in the training set and the number of predicted classes. This study investigates how these factors influence the model’s ability to defend against natural adversarial examples. Results indicate that reducing the number of training classes enhances the model’s defense against natural adversarial examples. Additionally, under a fixed number of training classes, some CNN models show an optimal range of predicted classes for achieving the best defense performance against these adversarial examples.展开更多
Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples ca...Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input data.In this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial examples.Additionally,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field.展开更多
Deep neural networks are known to be vulnerable to adversarial attacks.Unfortunately,the underlying mechanisms remain insufficiently understood,leading to empirical defenses that often fail against new attacks.In this...Deep neural networks are known to be vulnerable to adversarial attacks.Unfortunately,the underlying mechanisms remain insufficiently understood,leading to empirical defenses that often fail against new attacks.In this paper,we explain adversarial attacks from the perspective of robust features,and propose a novel Generative Adversarial Network(GAN)-based Robust Feature Disentanglement framework(GRFD)for adversarial defense.The core of GRFD is an adversarial disentanglement structure comprising a generator and a discriminator.For the generator,we introduce a novel Latent Variable Constrained Variational Auto-Encoder(LVCVAE),which enhances the typical beta-VAE with a constrained rectification module to enforce explicit clustering of latent variables.To supervise the disentanglement of robust features,we design a Robust Supervisory Model(RSM)as the discriminator,sharing architectural alignment with the target model.The key innovation of RSM is our proposed Feature Robustness Metric(FRM),which serves as part of the training loss and synthesizes the classification ability of features as well as their resistance to perturbations.Extensive experiments on three benchmark datasets demonstrate the superiority of GRFD:it achieves 93.69%adversarial accuracy on MNIST,77.21%on CIFAR10,and 58.91%on CIFAR100 with minimal degradation in clean accuracy.Codes are available at:(accessed on 23 July 2025).展开更多
Bridge pier failures from granular flow impacts are common.Installing defense piles upstream is an effective mitigation strategy,yet their protective mechanisms and standardized design guidelines are unclear.This stud...Bridge pier failures from granular flow impacts are common.Installing defense piles upstream is an effective mitigation strategy,yet their protective mechanisms and standardized design guidelines are unclear.This study employed 3D discrete element method to analyze the influence of defense pile size and placement on its performance across 219 scenarios,providing a detailed examination of their protective mechanisms.Results show that optimizing these factors can reduce the maximum impact force on bridge piers by up to 94%.In terms of size,a critical height threshold is identified,beyond which increasing pile height does not enhance protection.This threshold depends on the movement height of granular particles at the slope base.Protection effectiveness varies with pile size:when H≤0.05 h(H is the height of defense piles,h is the height of bridge),protection marginally improves with increasing height and diameter;for 0.05 h<H<0.15 h,protection strongly correlates with both parameters;for H≥0.15 h,diameter becomes the dominant factor.In terms of placement,an optimal longitudinal distance exists between the defense pile and the bridge pier.The larger the diameter,the greater the optimal longitudinal distance.However,the transverse distance is inversely related to protection effectiveness.Mechanistic analysis shows that defense piles are more effective at redirecting particles to prevent direct collisions with the pier(contributing 100%impact energy reduction before the non-dimensional travel time t*=7.01 and 63%–100%afterward)than at reducing particle velocity.This study provides insights into the protective mechanisms of defense piles and informs strategies for optimizing bridge pier protection in granular flow-prone regions.展开更多
Predator dummies are usually used to explore the impact of predator features on the anti-predator behavior of birds.Previous studies have shown that the morphology and behavior of aerial predators can signal different...Predator dummies are usually used to explore the impact of predator features on the anti-predator behavior of birds.Previous studies have shown that the morphology and behavior of aerial predators can signal different threat levels to birds.However,whether subtle changes in ground predator dummies cause changes in the nest defense behavior of parent birds is unclear.In this study,we aimed to investigate whether Japanese Tits(Parus minor)exhibit different nest defense behaviors in response to experimentally manipulated variations in the appearance,posture,and size of virtual snake proxies(common nest predators).During the incubation period,we observed the nest defense behaviors of the parent tits against taxidermized Siberian Ratsnakes(Elaphe schrenckii)with varied characteristics and rubber-made model snakes.The tits exhibited more intense responses to taxidermized large(body length about 120 cm)coiled ratsnakes than to large coiled model snakes.They exhibited weaker responses to taxidermized small(body length about 20 cm)coiled ratsnakes than to taxidermized small sinusoidal ratsnakes.In addition,they exhibited more intense responses to taxidermized large coiled ratsnakes than to taxidermized small coiled ratsnakes,and more intense responses to taxidermized small sinusoidal ratsnakes than to large model snakes.However,there was no difference in the response of tits to taxidermized small sinusoidal ratsnakes and taxidermized large coiled ratsnakes,or to taxidermized small coiled ratsnakes and model snakes.Thus,the presence of scales,a sinusoidal posture,and a large body size of snake dummies can induce more intense behavioral responses in Japanese Tits.We suggested that Japanese Tits can discriminate subtle differences in ground predator dummies of nests and exhibit different nest defense behaviors.展开更多
Deep neural networks(DNNs)have found extensive applications in safety-critical artificial intelligence systems,such as autonomous driving and facial recognition systems.However,recent research has revealed their susce...Deep neural networks(DNNs)have found extensive applications in safety-critical artificial intelligence systems,such as autonomous driving and facial recognition systems.However,recent research has revealed their susceptibility to backdoors maliciously injected by adversaries.This vulnerability arises due to the intricate architecture and opacity of DNNs,resulting in numerous redundant neurons embedded within the models.Adversaries exploit these vulnerabilities to conceal malicious backdoor information within DNNs,thereby causing erroneous outputs and posing substantial threats to the efficacy of DNN-based applications.This article presents a comprehensive survey of backdoor attacks against DNNs and the countermeasure methods employed to mitigate them.Initially,we trace the evolution of the concept from traditional backdoor attacks to backdoor attacks against DNNs,highlighting the feasibility and practicality of generating backdoor attacks against DNNs.Subsequently,we provide an overview of notable works encompassing various attack and defense strategies,facilitating a comparative analysis of their approaches.Through these discussions,we offer constructive insights aimed at refining these techniques.Finally,we extend our research perspective to the domain of large language models(LLMs)and synthesize the characteristics and developmental trends of backdoor attacks and defense methods targeting LLMs.Through a systematic review of existing studies on backdoor vulnerabilities in LLMs,we identify critical open challenges in this field and propose actionable directions for future research.展开更多
As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respo...As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker’s journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses’ need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. “Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises” is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.展开更多
The defense mechanisms induced in wild Chinese pine(Pinus tabuliformis)in response to herbivores are not well characterized,especially in the field.To address this knowledge gap,we established a biological model syste...The defense mechanisms induced in wild Chinese pine(Pinus tabuliformis)in response to herbivores are not well characterized,especially in the field.To address this knowledge gap,we established a biological model system to evaluate proteome variations in pine needles after feeding by the Chinese pine caterpillar(Dendrolimus tabulaeformis),a major natural enemy and dominant herbivore.Quantitative tandem mass tag(TMT)proteomics and bioinformatics were utilized to systematically identify differentially abundant proteins implicated in the induced defense response of Chinese pine.We validated key protein changes using parallel reaction monitoring(PRM)technology.Pathway analysis revealed that the induced defenses involved phenylpropanoid,coumarin,and flavonoid biosynthesis,among other processes.To elucidate the regulatory patterns underlying pine resistance,we determined the activities of defense enzymes and levels of physiological and biochemical compounds.In addition,the expression of upstream genes for key proteins was validated by qRT-PCR.Our results provide new molecular insights into the induced defense mechanisms in Chinese pine against this caterpillar in the field.A better understanding of these defense strategies will inform efforts to breed more-resistant pine varieties.展开更多
文摘Unlike most plants, members of the genus Solanum produce cholesterol and use this as a precursor for steroidal glycoalkaloids. The production of the compounds begins as a branch from brassinosteroid biosynthesis, which produces cholesterol that is further modified to produce steroidal glycoalkaloids. During the cholesterol biosynthesis pathway, genetic engineering could alter the formation of cholesterol from provitamin D3(7-dehydrocholesterol) and produce vitamin D3. Cholesterol is a precursor for many steroidal glycoalkaloids, including a-tomatine and esculeoside A. Alpha-tomatine is consumed by mammals and it can reduce cholesterol content and improve LDL:HDL ratio. When there is a high a-tomatine content, the fruit will have a bitter flavor, which together with other steroidal glycoalkaloids serving as protective and defensive compounds for tomato against insect, fungal, and bacterial pests. These compounds also affect the rhizosphere bacteria by recruiting beneficial bacteria. One of the steroidal glycoalkaloids, esculeoside A increases while fruit ripening. This review focuses on recent studies that uncovered key reactions of the production of cholesterol and steroidal glycoalkaloids in tomato connecting to human health, fruit flavor, and plant defense and the potential application for tomato crop improvement.
基金supported by the National Key Research and Development Program of China(2023YFD1200503 to Shuai Ma and 2021YFD1200700 to Tianyu Wang)。
文摘Highlights ZmMYC2 promoter contains favorable haplotypes selected during domestication,enhancing its expression level in modern maize.ZmMYC2 may balance the trade-off between growth and defense via jasmonate and auxin signaling pathways.ZmMYC2 regulates drought-response genes(CER2 and TIP3c)to optimize drought stress resilience.
基金funded in part by the Humanities and Social Sciences Planning Foundation of Ministry of Education of China under Grant No.24YJAZH123National Undergraduate Innovation and Entrepreneurship Training Program of China under Grant No.202510347069the Huzhou Science and Technology Planning Foundation under Grant No.2023GZ04.
文摘The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address this critical challenge,this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient(ZSG-MAD3PG).The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient(MAD3PG)algorithm and incorporates defensive deception(DD)strategies to achieve adaptive and efficient protection.While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,the proposed ZSG-MAD3PG framework mitigates these limitations through multi-stage game modeling and adaptive learning,enabling more efficient resource utilization and faster response times.The Stackelberg-based architecture allows defenders to dynamically optimize packet sampling strategies,while attackers adjust their tactics to reach rapid equilibrium.Furthermore,dynamic deception techniques reduce the time required for the concealment of attacks and the overall system burden.A lightweight behavioral fingerprinting detection mechanism further enhances real-time zero-day attack identification within industrial device clusters.ZSG-MAD3PG demonstrates higher true positive rates(TPR)and lower false alarm rates(FAR)compared to existing methods,while also achieving improved latency,resource efficiency,and stealth adaptability in IIoT zero-day defense scenarios.
基金supported jointly by the earmarked fund for CARS-10-GW2the key research and development program of Hainan Province(Grant No.ZDYF2020226)+1 种基金Collaborative innovation center of Nanfan and high-efficiency tropical agriculture,Hainan University(Grant No.XTCX2022NYC21)funding of Hainan University[Grant No.KYQD(ZR)22123]。
文摘Heat stress hinders the growth and productivity of sweetpotato plants,predominantly through oxidative damage to cellular membranes.Therefore,the development of efficient approaches for mitigating heat-related impairments is essential for the long-term production of sweetpotatoes.Melatonin has been recognised for its capacity to assist plants in dealing with abiotic stress conditions.This research aimed to investigate how different doses of exogenous melatonin influence heat damage in sweetpotato plants.Heat stress drastically affected shoot and root fresh weight by 31.8 and 44.5%,respectively.This reduction resulted in oxidative stress characterised by increased formation of hydrogen peroxide(H_(2)O_(2))by 804.4%,superoxide ion(O_(2)^(·-))by 211.5%and malondialdehyde(MDA)by 234.2%.Heat stress also reduced chlorophyll concentration,photosystemⅡefficiency(F_v/F_m)by 15.3%and gaseous exchange.However,pre-treatment with 100μmol L^(-1)melatonin increased growth and reduced oxidative damage to sweetpotato plants under heat stress.In particular,melatonin decreased H_(2)O_(2),O_(2)^(·-)and MDA by 64.8%,42.7%and 38.2%,respectively.Melatonin also mitigated the decline in chlorophyll levels and improved stomatal traits,gaseous exchange and F_(v)/F_(m)(13%).Results suggested that the favorable outcomes of melatonin treatment can be associated with elevated antioxidant enzyme activity and an increase in non-enzymatic antioxidants and osmo-protectants.Overall,these findings indicate that exogenous melatonin can improve heat stress tolerance in sweetpotatoes.This stu dy will assist re searchers in further investigating how melatonin makes sweetpotatoes more resistant to heat stress.
基金supported by the National Natural Science Foundation of China(No.32202963)the Natural Science Foundation of Jiangsu Province(No.BK20220681)+3 种基金the Doctoral Program of Entrepreneurship and Innovation in Jiangsu Province(No.JSSCBS20221625)the Scientific Research Foundation Program of Jiangsu Ocean University(No.KQ22009)the Undergraduate Innovation&Entrepreneurship Training Program of Jiangsu Province,China(No.SY202411641631001)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX2023-112).
文摘Due to the discharge of industrialwastewater,urban domestic sewage,and intensive marine aquaculture tailwater,nitrate(NO_(3)^(−))pollution has emerged as a significant issue in offshore waters.Nitrate pollution affects aquatic life and may interact with other pollutants,leading to comprehensive toxicity.Cadmium(Cd^(2+))is the most widespread metal contaminant,adversely affecting aquatic life in the coastal waters of China.Despite this,few studies have focused on the synergistic toxicity of NO_(3)^(−)and Cd^(2+)in marine organisms.This study conducted a 30-day exposure experiment on marine Japanese flounder(Paralichthys olivaceus)to explore the synergistic toxicity of NO_(3)^(−)and Cd^(2+).Our results demonstrated that the exposure to Cd^(2+)alone induced slight histopathological changes in the liver.However,malformations such as hepatic vacuolar degeneration and sinusoid dilatationwere exacerbated under co-exposure.Moreover,co-exposure induced the downregulation of antioxidants and the upregulation of the product malonaldehyde(MDA)from lipid peroxidation,indicating potent oxidative stress in the liver.The increased mRNA expression of IL-8,TNF-α,and IL-1β,along with the decreased expression level of TGF-β,indicated a synergistic inflammatory response in the organisms.Furthermore,the co-exposure led to an abnormal expression of P53,caspase-3,caspase-9,Bcl-2,and Bax,and disturbed the apoptosis in the liver through TUNEL staining analysis.Overall,our results imply that co-exposure synergistically affects inflammation,redox status,and apoptosis in flounders.Therefore,the findings from this study provide valuable perspectives on the ecological risk assessment of marine teleosts co-exposure to NO_(3)^(−)and Cd^(2+).
文摘In recent years,China and Indonesia have made notable progress in multiple areas of security cooperation,and their collaboration in this respect has continued to deepen under the leadership of both countries.In November 2024,China and Indonesia issued a joint statement during Indonesian President Prabowo Subianto’s visit to China.It was his first overseas trip after his inauguration.In the statement,the two countries agreed to add security cooperation as the fifth pillar of their partnership,marking an upgrade of bilateral ties.
文摘In a Nature Physics report published in late September 2024[1],a team of scientists and engineers at Sandia National Laboratories(Albuquerque,NM,USA)described the results of a laboratory experiment showing that a nuclear blast could create a burst of X-rays powerful enough to change the path of a large asteroid that might one day be on a collision course with Earth.
基金supported by the Excellent Youth Science Project of Henan Natural Science Foundation(242300421110)the National Natural Science Foundation of China(32372129,32272038)Henan Provincial Nature Foundation Project(242300420151).
文摘Benzoxazinoids(BXDs)are a class of plant secondary metabolites that play pivotal roles in plant defense against pathogens and pests,as well as in allelopathy.This review synthesizes recent advances in our understanding of the structural and functional diversity of BXDs,the independent evolutionary trajectories of their biosynthetic pathways across different plant species,their metabolic transformations in target organisms,and the opportunities and challenges of optimizing BXD biosynthesis in crops through metabolic engineering.Compared with monocotyledons,dicotyledons employ a more diverse set of enzymes to catalyze the core reactions of BXD biosynthesis.This functional divergence—yet biochemical convergence—between monocotyledons and dicotyledons exemplifies the convergent evolution of BXD biosynthetic pathways in plants.BXDs act not only as potent antifeedants,insecticides,and antimicrobials but also function as signaling molecules that induce callose deposition and activate systemic immunity,thereby enhancing plant resistance to biotic stress.Furthermore,BXDs shape the rhizosphere by modulating microbial communities through species-specific antimicrobial activities and microbial detoxification mechanisms,ultimately exerting allelopathic effects that alter soil chemistry and nutrient dynamics.The translational potential of BXDs is increasingly recognized by synthetic biology approaches,including artificial intelligence-driven enzyme optimization,heterologous pathway engineering,and gene-editing to enhance crop resistance.Despite these promising prospects,challenges remain in balancing metabolic trade-offs and mitigating ecological risks associated with persistent accumulation of BXDs.Future research integrating multi-omics,evolutionary genomics,and microbiome studies will be essential to fully harness BXDs for sustainable crop improvement and reduced reliance on synthetic agrochemicals.
基金funded by the Key Research and Development Program of Zhejiang Province No.2023C01141the Science and Technology Innovation Community Project of the Yangtze River Delta No.23002410100suported by the Open Research Fund of the State Key Laboratory of Blockchain and Data Security,Zhejiang University.
文摘Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),to enhance structural reasoning,knowledge retrieval,and memory management.The expansion of their application scope imposes higher requirements on the robustness of GNNs.However,as GNNs are applied to more dynamic and heterogeneous environments,they become increasingly vulnerable to real-world perturbations.In particular,graph data frequently encounters joint adversarial perturbations that simultaneously affect both structures and features,which are significantly more challenging than isolated attacks.These disruptions,caused by incomplete data,malicious attacks,or inherent noise,pose substantial threats to the stable and reliable performance of traditional GNN models.To address this issue,this study proposes the Dual-Shield Graph Neural Network(DSGNN),a defense model that simultaneously mitigates structural and feature perturbations.DSGNN utilizes two parallel GNN channels to independently process structural noise and feature noise,and introduces an adaptive fusion mechanism that integrates information from both pathways to generate robust node representations.Theoretical analysis demonstrates that DSGNN achieves a tighter robustness boundary under joint perturbations compared to conventional single-channel methods.Experimental evaluations across Cora,CiteSeer,and Industry datasets show that DSGNN achieves the highest average classification accuracy under various adversarial settings,reaching 81.24%,71.94%,and 81.66%,respectively,outperforming GNNGuard,GCN-Jaccard,GCN-SVD,RGCN,and NoisyGNN.These results underscore the importance of multi-view perturbation decoupling in constructing resilient GNN models for real-world applications.
基金supported by the National Natural Science Foundation of China(31971314)the Open Fund of State Key Laboratory of Tea Plant Biology and Utilization(SKLTOF20210122).
文摘GAME15, a scaffold protein, orchestrates the biosynthesis of steroidal glycoalkaloids (a class of compounds with known defensive properties) and steroidal saponins (which contribute to plant defense) in Solanaceae plants, essential for their defense mechanisms. By assembling key enzymes at the endoplasmic reticulum, GAME15 ensures efficient metabolite production, preventing toxic intermediate diffusion. This breakthrough in plant defense biosynthesis opens opportunities for metabolic engineering, enabling the production of valuable metabolites in non-native hosts and offering potential strategies for crop protection, reducing the need for chemical pesticides.
基金funded by National Natural Science Foundation of China No.62302520.
文摘Moving Target Defense(MTD)necessitates scientifically effective decision-making methodologies for defensive technology implementation.While most MTD decision studies focus on accurately identifying optimal strategies,the issue of optimal defense timing remains underexplored.Current default approaches—periodic or overly frequent MTD triggers—lead to suboptimal trade-offs among system security,performance,and cost.The timing of MTD strategy activation critically impacts both defensive efficacy and operational overhead,yet existing frameworks inadequately address this temporal dimension.To bridge this gap,this paper proposes a Stackelberg-FlipIt game model that formalizes asymmetric cyber conflicts as alternating control over attack surfaces,thereby capturing the dynamic security state evolution of MTD systems.We introduce a belief factor to quantify information asymmetry during adversarial interactions,enhancing the precision of MTD trigger timing.Leveraging this game-theoretic foundation,we employMulti-Agent Reinforcement Learning(MARL)to derive adaptive temporal strategies,optimized via a novel four-dimensional reward function that holistically balances security,performance,cost,and timing.Experimental validation using IP addressmutation against scanning attacks demonstrates stable strategy convergence and accelerated defense response,significantly improving cybersecurity affordability and effectiveness.
基金support provided by the National Natural Science Foundation of China(No.62173274)the National Key R&D Program of China(No.2019YFA0405300)+3 种基金the Natural Science Foundation of Hunan Province of China(No.2021JJ10045)Shanghai Aerospace Science and Technology Innovation Fund,China(No.SAST2020-004)Postdoctoral Fellowship Program of CPSF(No.GZB20240989)the Open Research Subject of State Key Laboratory of Intelligent Game,China(No.ZBKF-24-01).
文摘In air combat,one effective way to counter an incoming missile attacking an aircraft is to launch a defense missile compared with traditional passive defense strategies such as decoy and electronic countermeasures.To address this issue,this paper proposes a three-body cooperative active defense guidance law with overload constraints from the perspective of a small speed ratio.First,a cooperative guidance-oriented model for active defense is established and linearized to provide a foundation for the design of the guidance law.Then,the essential quantity known as Zero-Effort-Miss(ZEM)is analyzed during the engagement process.In order to minimize the influence of inaccurate estimates of remaining flight time in the ZEM,the concept of Zero-Effort-Velocity(ZEV)is introduced.Subsequently,utilizing the sliding mode control method,the guidance law is designed by selecting the ZEM and ZEV as sliding mode surfaces,combined with the fast power reaching law,and its finite-time stability is analyzed using the Lyapunov method.Furthermore,to quantitatively evaluate the performance of the proposed active defense guidance law,the interception rendezvous angle index is introduced.The proposed active defense guidance law considers integrating information from the incoming missile,aircraft,and defense missile with fewer simplifications and assumptions,and ensures that the aircraft is effectively protected with less overload required for the defense missile.Finally,simulation experiments demonstrate the effectiveness and adaptability of the proposed active defense guidance law.
文摘The emergence of adversarial examples has revealed the inadequacies in the robustness of image classification models based on Convolutional Neural Networks (CNNs). Particularly in recent years, the discovery of natural adversarial examples has posed significant challenges, as traditional defense methods against adversarial attacks have proven to be largely ineffective against these natural adversarial examples. This paper explores defenses against these natural adversarial examples from three perspectives: adversarial examples, model architecture, and dataset. First, it employs Class Activation Mapping (CAM) to visualize how models classify natural adversarial examples, identifying several typical attack patterns. Next, various common CNN models are analyzed to evaluate their susceptibility to these attacks, revealing that different architectures exhibit varying defensive capabilities. The study finds that as the depth of a network increases, its defenses against natural adversarial examples strengthen. Lastly, Finally, the impact of dataset class distribution on the defense capability of models is examined, focusing on two aspects: the number of classes in the training set and the number of predicted classes. This study investigates how these factors influence the model’s ability to defend against natural adversarial examples. Results indicate that reducing the number of training classes enhances the model’s defense against natural adversarial examples. Additionally, under a fixed number of training classes, some CNN models show an optimal range of predicted classes for achieving the best defense performance against these adversarial examples.
基金Supported by the National Natural Science Foundation of China(U1903214,62372339,62371350,61876135)the Ministry of Education Industry University Cooperative Education Project(202102246004,220800006041043,202002142012)the Fundamental Research Funds for the Central Universities(2042023kf1033)。
文摘Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input data.In this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial examples.Additionally,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field.
基金funded by the National Natural Science Foundation of China Project"Research on Intelligent Detection Techniques of Encrypted Malicious Traffic for Large-Scale Networks"(Grant No.62176264).
文摘Deep neural networks are known to be vulnerable to adversarial attacks.Unfortunately,the underlying mechanisms remain insufficiently understood,leading to empirical defenses that often fail against new attacks.In this paper,we explain adversarial attacks from the perspective of robust features,and propose a novel Generative Adversarial Network(GAN)-based Robust Feature Disentanglement framework(GRFD)for adversarial defense.The core of GRFD is an adversarial disentanglement structure comprising a generator and a discriminator.For the generator,we introduce a novel Latent Variable Constrained Variational Auto-Encoder(LVCVAE),which enhances the typical beta-VAE with a constrained rectification module to enforce explicit clustering of latent variables.To supervise the disentanglement of robust features,we design a Robust Supervisory Model(RSM)as the discriminator,sharing architectural alignment with the target model.The key innovation of RSM is our proposed Feature Robustness Metric(FRM),which serves as part of the training loss and synthesizes the classification ability of features as well as their resistance to perturbations.Extensive experiments on three benchmark datasets demonstrate the superiority of GRFD:it achieves 93.69%adversarial accuracy on MNIST,77.21%on CIFAR10,and 58.91%on CIFAR100 with minimal degradation in clean accuracy.Codes are available at:(accessed on 23 July 2025).
基金supported by the National Natural Science Foundation of China(Grant numbers 41977233)。
文摘Bridge pier failures from granular flow impacts are common.Installing defense piles upstream is an effective mitigation strategy,yet their protective mechanisms and standardized design guidelines are unclear.This study employed 3D discrete element method to analyze the influence of defense pile size and placement on its performance across 219 scenarios,providing a detailed examination of their protective mechanisms.Results show that optimizing these factors can reduce the maximum impact force on bridge piers by up to 94%.In terms of size,a critical height threshold is identified,beyond which increasing pile height does not enhance protection.This threshold depends on the movement height of granular particles at the slope base.Protection effectiveness varies with pile size:when H≤0.05 h(H is the height of defense piles,h is the height of bridge),protection marginally improves with increasing height and diameter;for 0.05 h<H<0.15 h,protection strongly correlates with both parameters;for H≥0.15 h,diameter becomes the dominant factor.In terms of placement,an optimal longitudinal distance exists between the defense pile and the bridge pier.The larger the diameter,the greater the optimal longitudinal distance.However,the transverse distance is inversely related to protection effectiveness.Mechanistic analysis shows that defense piles are more effective at redirecting particles to prevent direct collisions with the pier(contributing 100%impact energy reduction before the non-dimensional travel time t*=7.01 and 63%–100%afterward)than at reducing particle velocity.This study provides insights into the protective mechanisms of defense piles and informs strategies for optimizing bridge pier protection in granular flow-prone regions.
基金sponsored by National Natural Science Foundation of China(No.32271560 to H.W.,32001094 to J.Y.)Natural Science Foundation of Jilin Province,China(No.20230101160JC to L.J.)+1 种基金the Open Project of Ministry of Education Key Laboratory for Ecology of Tropical Islands,Hainan Normal University,China(No.HNSF-OP-202301 to J.Y.)the Fundamental Research Funds for the Central Universities(No.2412022ZD019 to J.Y.)。
文摘Predator dummies are usually used to explore the impact of predator features on the anti-predator behavior of birds.Previous studies have shown that the morphology and behavior of aerial predators can signal different threat levels to birds.However,whether subtle changes in ground predator dummies cause changes in the nest defense behavior of parent birds is unclear.In this study,we aimed to investigate whether Japanese Tits(Parus minor)exhibit different nest defense behaviors in response to experimentally manipulated variations in the appearance,posture,and size of virtual snake proxies(common nest predators).During the incubation period,we observed the nest defense behaviors of the parent tits against taxidermized Siberian Ratsnakes(Elaphe schrenckii)with varied characteristics and rubber-made model snakes.The tits exhibited more intense responses to taxidermized large(body length about 120 cm)coiled ratsnakes than to large coiled model snakes.They exhibited weaker responses to taxidermized small(body length about 20 cm)coiled ratsnakes than to taxidermized small sinusoidal ratsnakes.In addition,they exhibited more intense responses to taxidermized large coiled ratsnakes than to taxidermized small coiled ratsnakes,and more intense responses to taxidermized small sinusoidal ratsnakes than to large model snakes.However,there was no difference in the response of tits to taxidermized small sinusoidal ratsnakes and taxidermized large coiled ratsnakes,or to taxidermized small coiled ratsnakes and model snakes.Thus,the presence of scales,a sinusoidal posture,and a large body size of snake dummies can induce more intense behavioral responses in Japanese Tits.We suggested that Japanese Tits can discriminate subtle differences in ground predator dummies of nests and exhibit different nest defense behaviors.
基金supported in part by the National Natural Science Foundation of China under Grants No.62372087 and No.62072076the Research Fund of State Key Laboratory of Processors under Grant No.CLQ202310the CSC scholarship.
文摘Deep neural networks(DNNs)have found extensive applications in safety-critical artificial intelligence systems,such as autonomous driving and facial recognition systems.However,recent research has revealed their susceptibility to backdoors maliciously injected by adversaries.This vulnerability arises due to the intricate architecture and opacity of DNNs,resulting in numerous redundant neurons embedded within the models.Adversaries exploit these vulnerabilities to conceal malicious backdoor information within DNNs,thereby causing erroneous outputs and posing substantial threats to the efficacy of DNN-based applications.This article presents a comprehensive survey of backdoor attacks against DNNs and the countermeasure methods employed to mitigate them.Initially,we trace the evolution of the concept from traditional backdoor attacks to backdoor attacks against DNNs,highlighting the feasibility and practicality of generating backdoor attacks against DNNs.Subsequently,we provide an overview of notable works encompassing various attack and defense strategies,facilitating a comparative analysis of their approaches.Through these discussions,we offer constructive insights aimed at refining these techniques.Finally,we extend our research perspective to the domain of large language models(LLMs)and synthesize the characteristics and developmental trends of backdoor attacks and defense methods targeting LLMs.Through a systematic review of existing studies on backdoor vulnerabilities in LLMs,we identify critical open challenges in this field and propose actionable directions for future research.
文摘As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker’s journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses’ need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. “Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises” is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.
基金supported by the Science and Technology Development Program of Hebei Agricultural University,the Research on Molecular Mechanisms of Population Differentiation and Adaptation of Forest Pests and Insects under Environmental Stress(grant No.:30771739)Forest Pests and Diseases(grant No.:1528003)the National Natural Science Foundation of China for the study of community regulatory mechanisms of insect pest pandemics in larch plantation forests(Grant No.:32371882).
文摘The defense mechanisms induced in wild Chinese pine(Pinus tabuliformis)in response to herbivores are not well characterized,especially in the field.To address this knowledge gap,we established a biological model system to evaluate proteome variations in pine needles after feeding by the Chinese pine caterpillar(Dendrolimus tabulaeformis),a major natural enemy and dominant herbivore.Quantitative tandem mass tag(TMT)proteomics and bioinformatics were utilized to systematically identify differentially abundant proteins implicated in the induced defense response of Chinese pine.We validated key protein changes using parallel reaction monitoring(PRM)technology.Pathway analysis revealed that the induced defenses involved phenylpropanoid,coumarin,and flavonoid biosynthesis,among other processes.To elucidate the regulatory patterns underlying pine resistance,we determined the activities of defense enzymes and levels of physiological and biochemical compounds.In addition,the expression of upstream genes for key proteins was validated by qRT-PCR.Our results provide new molecular insights into the induced defense mechanisms in Chinese pine against this caterpillar in the field.A better understanding of these defense strategies will inform efforts to breed more-resistant pine varieties.
